Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

AGI / Artificial General Intelligence Artificial Intelligence (AI)Attention Algorithm Autoencoder

B

BERT Boosting Bias Backpropagation Batch Normalization

C

Cross-Validation Classifier / Classification CNN / Convolutional Neural Network Chatbot Clustering

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

Forward Propagation Foundation Model Fine-tuning Feature Extraction Fusion / Multimodal Fusion

G

GAN / Generative Adversarial Network Grounding Graph Neural Network (GNN)Gradient Descent Generative AI

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation Interpretability

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)K-means Clustering Knowledge Distillation

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Learning Rate Loss Function Latent Variable

M

Meta-learning Multimodal / Multimodality Multi-head Attention Model Machine Learning (ML)

N

NLU / Natural Language Understanding Novelty Detection / Anomaly Detection Normalization Neural Network NLP / Natural Language Processing

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

RNN / Recurrent Neural Network Reinforcement Learning (RL)Retrieval Augmented Generation (RAG)Regularization Representation Learning

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

Vision Transformer (ViT)Vector Embedding Vanishing / Exploding Gradient Variational Autoencoder (VAE)Validation Set

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

YAGNI (You Aren't Gonna Need It)Yoga of AI Y-transform / YUV Y-axis / feature axis Yield (model yield / throughput)

Z

Z-score Normalization Zygosity in augmentation Zero-centric / Zero-bias initialization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference

什麼是XOR問題

XOR（異或）問題是計算機科學和機器學習中的一個經典問題，涉及到邏輯運算中的“異或”操作。在二元邏輯中，XOR運算返回真值，當輸入的兩個布爾值不同时（即一個為真，另一個為假）。這個問題在神經網絡和深度學習的背景下尤為重要，因為它展示了簡單的線性模型無法處理的非線性特徵。

XOR問題的經典實例是二進制輸入的組合（0或1），其輸出的規則是：輸入(0,0)和(1,1)的輸出為0，而輸入(0,1)和(1,0)的輸出為1。這種簡單的邏輯關係在簡單的神經網絡（如單層感知器）中無法正確學習，因為單層感知器只能表示線性可分的模式。

在機器學習的歷史中，XOR問題曾是神經網絡發展的一個重要里程碑。早期的研究表明，深度學習模型（即多層神經網絡）能夠有效地解決XOR問題，證明了深度神經網絡的強大能力和適用性。在未來，XOR問題的研究將繼續推動非線性模型和複雜數據結構的開發。

優點方面，XOR問題使得我們能夠更好地理解和設計複雜的機器學習模型；缺點在於，過於簡單的模型在面對這一問題時會顯得無能為力。相關注意事項包括在模型設計時要考慮輸入數據的特性，並選擇合適的網絡結構和激活函數。